Study on Priming Effects of Different Chemical Initiators on Chinese Cabbage Seeds
Bibliographic record
Abstract
To determine the chemical initiator's priming effects on the Chinese cabbage seeds,the Chinese cabbage cultivar Shu Lv and Han Xiao seeds were selected as materials,and used different types and different concentrations of chemical reagents to priming the Chinese cabbage seeds.The results showed that in the chloride ion,phosphate ion and nitrate ion chemical initiator,the priming effects of chloride salt solution were similar to the nitrate salt solution,both priming effects were more significant than that of the phosphate ion salt solution.The priming effect of 1% CaCl2 among chloride salt solution was better relatively,Shu Lv germination energy and germination rate reached 90.67% and 94.67%,respectively.Han Xiao germination energy and germination rate were respectively up to 85.33% and 85.33%,and seed germination index and vigor index significantly exceeded the control and other treatments.Of Nitrate salt solution,0.5% KNO3 in germination energy,germination rate,germination index and vigor index were significantly beyond the control and other treatments.Shu Lv respectively got germination energy of 90.00% and germination rate of 90.67%.Han Xiao obtained germination energy of 88.00% and germination rate of 92.00%,while the priming effects of the phosphate ion salt solution(except 1% KH2PO4) were not ideal.Different concentrations of PEG priming treatment significantly increased the seed germination energy,germination rate and vigor index,but no significant effect on germination index.Comprehensive comparison effect of various chemical initiators on seed germination,1% CaCl2 came the first,0.5% KNO3 ranked the second,and the third was 10% PEG,while K3PO4 had the worst priming effects,and leading to seed germination abnormal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".